Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI

Autor: Zarina Ramli, Muhammad Khalis Abdul Karim, Nuraidayani Effendy, Mohd Amiruddin Abd Rahman, Mohd Mustafa Awang Kechik, Mohamad Johari Ibahim, Nurin Syazwina Mohd Haniff
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Diagnostics, Vol 12, Iss 12, p 3125 (2022)
Druh dokumentu: article
ISSN: 2075-4418
DOI: 10.3390/diagnostics12123125
Popis: Cervical cancer is the most common cancer and ranked as 4th in morbidity and mortality among Malaysian women. Currently, Magnetic Resonance Imaging (MRI) is considered as the gold standard imaging modality for tumours with a stage higher than IB2, due to its superiority in diagnostic assessment of tumour infiltration with excellent soft-tissue contrast. In this research, the robustness of semi-automatic segmentation has been evaluated using a flood-fill algorithm for quantitative feature extraction, using 30 diffusion weighted MRI images (DWI-MRI) of cervical cancer patients. The relevant features were extracted from DWI-MRI segmented images of cervical cancer. First order statistics, shape features, and textural features were extracted and analysed. The intra-class relation coefficient (ICC) was used to compare 662 radiomic features extracted from manual and semi-automatic segmentations. Notably, the features extracted from the semi-automatic segmentation and flood filling algorithm (average ICC = 0.952 0.009, p > 0.05) were significantly higher than the manual extracted features (average ICC = 0.897 0.011, p > 0.05). Henceforth, we demonstrate that the semi-automatic segmentation is slightly expanded to manual segmentation as it produces more robust and reproducible radiomic features.
Databáze: Directory of Open Access Journals
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